Description

As an AI and Machine Learning Engineer, you will be responsible for developing advanced generative AI models, fine-tuning pre-trained models, and working on data preprocessing. Collaboration with cross-functional teams is key, and expertise in graph neural networks is required. Experimentation with model architectures, adherence to ML engineering best practices, and utilization of tools like Model Garden and AWS SageMaker are essential. Staying current with AI research is vital for ongoing technology improvement and innovation. KEY RESPONSIBILITIES Design, develop, and deploy advanced generative AI models using techniques such as BERT, Transformers architecture, and other deep learning methodologies. Utilize pre-trained models from libraries like Hugging Face to fine-tune and adapt models for specific tasks and domains. Work on data preprocessing, feature extraction, and engineering to prepare datasets for training and testing. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project goals and requirements. Apply expertise in graph neural networks to develop, optimize, and implement graph-based models such as Variational Graph Autoencoders (VGAE). Experiment with various model architectures, hyperparameters, and techniques to improve model performance and achieve desired outcomes. Apply best practices in machine learning engineering, including model versioning, testing, and documentation. Utilize tools such as Model Garden and AWS SageMaker for model development and deployment. Stay up to date with the latest advancements in AI and machine learning research to continuously improve our technology stack. KEY REQUIREMENTS Bachelors or masters degree in computer science, Engineering, or related field. 2-4 years of experience as a Machine Learning Engineer, with a focus on generative AI models and graph neural networks. Proficiency in working with deep learning frameworks such as TensorFlow or PyTorch. Strong experience with generative models such as BERT, GPT, and Transformers, and familiarity with Hugging Face models. Prior experience in deploying machine learning models using tools like Model Garden, AWS SageMaker, or similar. Hands-on experience with graph neural network models such as GraphSAGE, VGAE, or other relevant architectures. Solid understanding of machine learning principles, algorithms, and methodologies. Proficiency in programming languages such as Python, and experience with libraries like NumPy, Pandas, etc. Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. Excellent communication skills and ability to explain complex concepts to non-technical stakeholders. PREFERRED QUALIFICATION Publications, research, or contributions to the machine learning community. Familiarity with natural language processing (NLP) techniques and applications. Experience with distributed computing and model parallelism. Experience in the SaaS, inside sales, restaurant, hospitality, and food service industry. Knowledge of restaurant operations is always a plus but not necessary. Start-up experience (we love entrepreneurs!). Experience in working virtually and with a virtual team.

Role and Responsibilities

• As an AI and Machine Learning Engineer, you will be responsible for developing advanced generative AI models, fine-tuning pre-trained models, and working on data preprocessing. Collaboration with cross-functional teams is key, and expertise in graph neural networks is required. Experimentation with model architectures, adherence to ML engineering best practices, and utilization of tools like Model Garden and AWS SageMaker are essential. Staying current with AI research is vital for ongoing technology improvement and innovation. KEY RESPONSIBILITIES Design, develop, and deploy advanced generative AI models using techniques such as BERT, Transformers architecture, and other deep learning methodologies. Utilize pre-trained models from libraries like Hugging Face to fine-tune and adapt models for specific tasks and domains. Work on data preprocessing, feature extraction, and engineering to prepare datasets for training and testing. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project goals and requirements. Apply expertise in graph neural networks to develop, optimize, and implement graph-based models such as Variational Graph Autoencoders (VGAE). Experiment with various model architectures, hyperparameters, and techniques to improve model performance and achieve desired outcomes. Apply best practices in machine learning engineering, including model versioning, testing, and documentation. Utilize tools such as Model Garden and AWS SageMaker for model development and deployment. Stay up to date with the latest advancements in AI and machine learning research to continuously improve our technology stack. KEY REQUIREMENTS Bachelors or masters degree in computer science, Engineering, or related field. 2-4 years of experience as a Machine Learning Engineer, with a focus on generative AI models and graph neural networks. Proficiency in working with deep learning frameworks such as TensorFlow or PyTorch. Strong experience with generative models such as BERT, GPT, and Transformers, and familiarity with Hugging Face models. Prior experience in deploying machine learning models using tools like Model Garden, AWS SageMaker, or similar. Hands-on experience with graph neural network models such as GraphSAGE, VGAE, or other relevant architectures. Solid understanding of machine learning principles, algorithms, and methodologies. Proficiency in programming languages such as Python, and experience with libraries like NumPy, Pandas, etc. Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. Excellent communication skills and ability to explain complex concepts to non-technical stakeholders. PREFERRED QUALIFICATION Publications, research, or contributions to the machine learning community. Familiarity with natural language processing (NLP) techniques and applications. Experience with distributed computing and model parallelism. Experience in the SaaS, inside sales, restaurant, hospitality, and food service industry. Knowledge of restaurant operations is always a plus but not necessary. Start-up experience (we love entrepreneurs!). Experience in working virtually and with a virtual team.